Imaging in low light is challenging due to low photon count and low SNR.
Short-exposure images suffer from noise, while long exposure can induce blur
and is often impractical. A variety of denoising, deblurring, and enhancement
techniques have been proposed, but their effectiveness is limited in extreme
conditions, such as video-rate imaging at night. To support the development of
learning-based pipelines for low-light image processing, we introduce a dataset
of raw short-exposure low-light images, with corresponding long-exposure
reference images. Using the presented dataset, we develop a pipeline for
processing low-light images, based on end-to-end training of a
fully-convolutional network. The network operates directly on raw sensor data
and replaces much of the traditional image processing pipeline, which tends to
perform poorly on such data. We report promising results on the new dataset,
analyze factors that affect performance, and highlight opportunities for future
work. The results are shown in the supplementary video at
https://youtu.be/qWKUFK7MWvg

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Made with a human heart + one part enriched uranium + four parts unicorn blood